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32 to Figure 3.8, should be considered to be approximately con- Forecasting models most often deal with average weekday stant at 6 percent each hour for all hours from 1:00 A.M. to conditions. From Figure 3.11, the average daily flow is 85 per- 5:00 P.M. This drops to only 5.5 percent in each of the hours cent of the average weekday flow. The annual conversion to before and after that period. Although this may appear incon- average weekday would then be 85 percent of the days in the sistent with conventional wisdom that trucks travel at night, it year, or 310. It also is observed that there is variation in truck should be recognized that the national average truck trip length volumes during the week, with similar volumes on Tuesday reported in the 2002 CFS is only 173 mi. At this distance, which through Thursday and lower truck volumes on Mondays and can easily be completed during one business day, it should be Fridays. The average of Tuesday through Thursday truck vol- expected that the majority of truck commodity activity would umes is 81 percent of the average daily volumes. The annual occur during normal business hours, if not prevented by local conversion to midweek daily flows would be 81 percent of conditions. 365 days or 295. It should be noted that other qualitative estimates of the conversion of annual to daily flows have relied on evidence Annual-to-Daily Factor from other sources in which total flow on both weekend days The assumption of this research was that different commodi- is almost equal to the average weekday flow which, over ties have different seasonal and temporal variations. Because the 52 weeks, would mean a flow equivalent to 312 days. This is finding was that, on average, all commodities have similar sea- often reduced by an estimate of the number of holidays on sonal and temporal variations, the VTRIS data were exam- which little flow is expected, a number ranging from 6 to ined to address an additional issue--the factor that should be 12 days, which would reduce the annual-to-daily conversion applied to all annual ton flows to convert to daily flows. to a number between 300 and 306. This practice would appear Different practitioners use different adjustments to con- to be consistent with the values derived from Figure 3.11. vert annual commodity flows to daily flows. Some practition- ers merely divide the annual tons by 365, the number of days 3.5 Developing Mode-Choice Models in a year, neglecting any lower flow on Saturdays and Sun- for Freight Forecasting days. Some practitioners divide the annual flows by 250 as an estimate of the number of working weekdays, which makes Policy decisions for freight commonly consider alternatives the assumption that there is no flow on Saturdays and Sun- that could change the mode-choice decision for domestic days. The proper factor is expected to fall within that range freight, most often those that would shift freight from high- that would imply there is flow on Saturdays and Sundays but way modes (truck) to nonhighway modes (rail, inland water, that the flow is less than the flow occurring on weekdays. or air). These policy alternatives would benefit from a better The VTRIS data, for those stations that had continuous understanding of the factors that affect the mode-choice deci- counts for the entire year, were examined to determine how sion for freight, including the relative importance of these fac- those truck volumes vary during an average week. The 542 mil- tors and how they should be considered in freight forecasting. lion trucks that were observed at the 177 stations with complete To address this need, this research topic investigated the vari- counts have the weekday distribution shown in Figure 3.11. ables used in mode-choice decisions and attempted to find 140% 120% 100% 80% 60% 40% 20% 0% Sun Mon Tue Wed Thu Fri Sat Figure 3.11. Weekday trucks at VTRIS stations as percentage of average daily trucks.

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33 how those variables can be used in estimating a freight mode- are separately reported for each modal component of a trip choice model. between an origin and a destination. Given these requirements, In choice modeling, equations representing the choice only a few commodity flow databases should be considered for decision are most often in the form of logit choice equations. use as RP databases, as follows: These logit models may be multinomial, that is several vari- ables are considered simultaneously in the choice decision, Databases that are limited to single modes (for example, and where there is no correlation between those variables. In the STB Carload Waybill Survey for rail) can not be used order to determine the variables that are important in the because they reveal no information about the decisions for choice decision, as well as to determine the relative impor- competing nonrail freight modes. tance of these variables, it is necessary to examine a survey of The publicly available CFS provides flows for both origins those choices together with the values of variables that are rel- and destinations only as state-to-state movements, and evant to those choices. When the survey reflects observed entire states are not a scale of geography over which modal choices as well as the observed relevant variables, this is called availability and characteristics can be considered similar. a revealed-preference (RP) survey. When the survey is made The privately available TRANSEARCH database does pro- of decisionmakers to determine their stated choices given a vide flows for seven modes between zones chosen as part of hypothetical set of values for relevant variables, this is called the data purchase, which can be as small as counties or, syn- a stated-preference (SP) survey. thetically, as zip codes. The flows are reported in unlinked Stated-preference surveys in the mode-choice decision for form, which although more suitable for determining the freight would require the identification of a statistically rele- proper assignment to modal networks, provides incom- vant sample of decisionmakers. An SP survey requires provid- plete information for trips that use multiple modes. Addi- ing those decisionmakers with sufficient hypothetical choice tionally, the cost of obtaining the entire TRANSEARCH experiments. From their responses, the relevant variables in database as county-to-county flows for the nation would the freight mode-choice decision would be determined. That be prohibitive. A single state database with flows at the determination would be made by estimating the coefficients county level within that state typically costs from $50,000 and parameters associated with those variables in a logit choice to $100,000 for a single year. model. The freight mode choice is most often national in The FAF2 database provides information for all modes in a scope, which would require that the geographic scope of such consistent format, including linked multimodal trips. How- a survey also be national in scope. Identifying these decision- ever, the zones in the FAF2 database are very large and some makers and conducting the choice experiments is an expen- modes--especially water and rail--cannot be expected to sive undertaking that is beyond the resources of all but the be uniformly available throughout these zones. largest freight studies. In order for RP surveys to support the development of a Although some records in the FAF2 commodity flow data- freight mode-choice model, it is necessary for the survey to base may not be suitable for use in an RP survey, it contains report flows for all modes in a consistent manner, over a period enough suitable records that it might be processed for use as of time that is long enough, and for a geography that is large an RP survey. enough to capture modal decisions. Because the values of the choice variables will differ between different origins and des- Variables in Freight Mode Choice tinations, the RP survey must report information for both the origins and destinations of freight. Because freight mode- A literature review was conducted to determine variables choice decisions are assumed to be similar for freight that that would be important in the mode-choice decision for shares the same characteristics, the freight flows also should be freight. Although not intended to be exhaustive, the variables reported separately for freight (e.g., by commodities) that is that were determined to be important in the mode-choice expected to behave similarly. Additionally, the choice variable decision for freight are and the observed decisions can not be expected to be the same over very large geographies. The reported geographies in the Characteristics of the mode, including capacity, trip time, RP database must be at a scale where modal availability and reliability, cost; modal characteristics can be assumed to be similar within the Characteristics of the goods, including shipment size, shelf reported geography. Finally, the choice in the RP database life, density, value; should be complete trips between and origin and destination-- Characteristics of the shipper, including production pro- that is, linked trips--that may involve several modes as well as cesses and shipper size; the transfers between modes at intermediate points. Mode- Characteristics of the receiver, including receiver size and choice decisions should not be made using unlinked trips that other consumption processes such as operating hours; and

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34 Other logistic characteristics, including shipment frequency, Detail by commodity, including shipment size and value inventory costs, loss and damage costs, etc. from the FAF2 commodity flow database; Employment by industry for the shipper and receiver regions Although these variables might be important in the mode- from the U.S. Census county business patterns; and choice decision for freight, determining values for many of Population by destination region from the U.S. Census. these variables requires detailed information about ship- ments, which might be obtained from an SP survey but can The utility equations developed for use in logit mode- not be expected to be available for all shipment records in an choice equations include a constant for each mode, expressed RP survey. Publicly available information was identified that as a difference from a base mode. The base mode, for which could be used to develop data for the variables to support the no modal constant will be estimated, is trucking. Separate use of an RP survey, including equations were developed for similar commodities. The generally important mode-choice variables, as well as Modal distances and impedances between U.S. counties how those variables will correspond to the publicly available from the Center for Transportation Analysis (CTA) at Oak data and parameters in the RP estimation, are shown in Ridge National Laboratory (ORNL); Table 3.6. Table 3.6. Freight mode-choice variables. Corresponding Variable to be Used in Revealed-Preference Category Utility Variable Utility Estimation Modal Characteristics Capacity Modal Constant Trip Time Modal Distance/Impedance Reliability Modal Constant Equipment Availability Modal Constant Customer Service and Handling Modal Constant Quality Modal Cost Modal Distance/Impedance Goods Characteristics Shipment Size Commodity Total Tons Package Characteristics Commodity Modal Constants Shipment Shelf Life Commodity Modal Constants Shipment Value Commodity Value per Ton Shipment Density Commodity Modal Constants Shipper Characteristics Production Processes Industry Employment at Origin Shipper Size Industry Employment at Origin Receiver Characteristics Consumption Requirements Industry Employment/Population at Destination Receiver Size Industry Employment/Population at Destination Other Logistic Inventory Costs Commodity Modal Constants Characteristics Loss and Damage Costs Commodity Modal Constants Service Reliability Costs Commodity Modal Constants Length of Haul Truck Distance Shipment Frequency Commodity Total Tons

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35 Preparing the FAF2 for Use as an RP Survey The FAF2 records that were included for use in an RP survey include those domestic flows between metropolitan regions, The FAF2 commodity flow database provides separate tables excluding flows that are reported as shipments within the for domestic freight flows, seaborne international freight flows, same metropolitan area. It is assumed that the flows reported land border crossing flows, and air and other international for these records are consistently available with the same char- modal flows. The designation of the international tables pro- acteristics for all modes. vides information about the mode used internationally (e.g., In order to use as an RP database, total flows between and sea, land border, or air) while the attributes within the table origin and destination, as well as the flows by each mode, provide information about the mode used for domestic flows must be determined. The FAF2 database was reformatted to between a U.S. FAF zone and the U.S. port of entry/exit. The include the flow by each mode for each origin, destination, seven zones outside of the United States are very large and and SCTG2 commodity. Appended to these were the vari- include the entire countries of Canada and Mexico, which ables for that origin and destination that were to be tested as leaves only five zones for all of the rest of the world. Addition- explanatory variables in the choices represented by the observed ally, these files are prepared from other commodity flow files modal flows. and it has been confirmed by the FHWA Office of Freight Modal distances were obtained from the CTA at ORNL. Management that the correspondence between the commod- The CTA provides skim tables of distances and impedances ity codes used in some of the international flow data files and between U.S. counties for highway (truck), rail, and water, as the SCTG commodities codes used in the FAF2 is not correct well great circle distances for air travel. These skim times are (e.g., SCTG 34, machinery, actually includes flows for all man- based on the paths identified using the ORNL modal freight ufactured products for international water flows and there are networks. In addition to distances, the CTA skims include no international water flows assigned to other SCTG codes for estimated impedances for each of the modes, as well as rail manufactured goods). Because of the errors in commodity highway rail (RHR) impedances that represent the impedance assignments, because the flows only include the domestic using the respective rail and highway networks connecting mode used, and because the international geographies are too through the intermodal terminals that are expected to serve large to ensure consistent modal characteristics and availabil- that origin and destination pair. ity for the entire international zone, it was determined that only These CTA distances and impedances are for U.S. county- the records in the FAF2 domestic tables would be suitable for to-county movements. In order to use these distances with use in an RP survey. the FAF-region-to-FAF-region records in the RP data, a rep- The FAF2 domestic table reports flows between 114 FAF2 resentative county had to be associated with each FAF region regions. These regions include the state portions of the largest included in the RP survey, which includes only FAF2 metro- metropolitan areas, as well as whole states, or remainders of politan regions. The county with the largest employment in a states outside of those metropolitan areas. The FAF2 regions FAF metropolitan region was chosen as the representative representing whole states, or remainders of states outside of county for use in selecting distance and impedances from the the metropolitan regions, are too large to ensure consistent CTA skim files. modal characteristics and availability throughout the region. County employment, the surrogate for shipper charac- Therefore, all records that contain a whole state or a remain- teristics, was obtained from county business patterns. The der of a state zone as an origin or destination were not included employment total for all of the counties in the FAF region for use in the RP survey. Finally, the separation of metro- was selected to test as an explanatory variable. In the same politan areas into their state portions was intended to aid in manner, the Census of Population was used to calculate the developing summaries of freight flows at the state level. The population of the region, the surrogate for receiver charac- reported FAF2 flows between FAF2 regions in the same met- teristics. From the FAF2 database itself, the total flow for the ropolitan area but in different states will involve short dis- O/D/C record was added for use as a surrogate for shipment tances over which modal choice decisions most likely reflect size. From that same FAF2 database, the value of the ship- production or logistic processes unique to the commodity ment by all modes also was added to the RP data for use as a and not decisions that should be considered in an RP survey. surrogate for shipment value. Therefore, records that are of freight flows within the same An investigation of the RP database indicated that the SCTG metropolitan area were not included for use in an RP survey. two-digit level for commodities had insufficient records to use Finally, while the FAF2 includes records for Hawaii and Alaska, in estimating models for some commodities. The records were the mode-choice decision for shipment to or from these aggregated to the commodity groups used in the 2002 CFS as regions includes unique considerations and modes, and they shown in Table 3.4 in order to provide sufficient records to were dropped from use in an RP survey. estimate the mode-choice equations by commodity group.

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36 Estimation of Mode-Choice Utility Equation surrogate of annual tons moving between markets, is only sig- nificant as a cross product with the natural logarithm of dis- Utility equations were estimated from the RP flows and the tance. This indicates that the impact of shipment size increases variables associated with those flows. The software used was as distance increases, but it does so at a logarithmically decreas- an object-oriented software package designed for the maxi- ing rate. The estimation also shows that value per ton is only mum likelihood estimation of generalized extreme value (GEV) significant as a cross product with the natural logarithm of models including multinomial logit models. distance. This indicates that the impact of value increases as Each of the RP variables listed in Table 3.4 were tested singly, distance increases but it does so at a logarithmically increas- as simple functions, as simple cross products (e.g., tons multi- ing rate. plied by distance, or as cross products functions with other The results of the estimation are shown in Table 3.7. The variables such as distance multiplied by the natural logarithm variables were chosen to provide, where possible, uniform of value per ton). The initial estimation runs were used to consistency across commodity groups. Thus, a chosen vari- determine which variables did not contribute significantly to able might have lower than desirable significance, as indicated the utility equations, as indicated by uniformly poor t-statistics. by its t-statistic where absolute values greater than 2.0 are gen- It was found that the surrogate for producer characteristics, erally considered to be significant. However, that variable was employment at the origin and for receiver characteristics, and retained to allow for comparison with other modes and com- population at the destination, were not significant explanatory modities. For those commodities where the variable clearly variables at the geographies tested. This does not necessarily degrades the estimation, they were excluded. indicate that these variables are unimportant, only that they are Although the estimation model was used primarily to show unimportant at the actual geographic scales as used in the RP which variables are significant in freight mode choice, the esti- survey. It is possible that at smaller geographic scales and for mated coefficients themselves can be used to gain insights as specific shippers (which, of course, would not be correlated to how mode-choice decisions might change as these variables with the total of all employment over an entire FAF region), change. these variables might be significant. The sign of the variable coefficients in Table 3.7 indicates The CTA impedances were found to be highly correlated whether the modal utility increases (has a positive sign) or with distance and, in fact, the CTA describes how they are com- decreases (has a negative sign) as the variable increases. Thus, puted from distances. Because impedances were so highly cor- for SCTG 01-05, agricultural products commodity group, the related with distance, only modal distances were retained as truck utility for distance decreases (coeff. = 0.00423/mi) as utility variables. distance increases. As expected and shown in Table 3.7, the Modal constants by commodity were estimated and found modal utility decreases as distance (serving as a surrogate for to be significant and large. However, because these modal con- modal cost and time) increases. stants are associated with a number of general variables, it is The value of the modal coefficient for all modes within a not possible to determine which of the general variables are the commodity group relative to other modes within that same most significant. Additionally, the estimation method only commodity group is an estimate of the relative utility of that provides an indication that these modal constants are signifi- mode to other modes. Thus for the agricultural products com- cant relative to an assumed zero value for the base mode, which modity group, the rail distance coefficient of 0.00397 (which was chosen to be "truck." It provides no indication of what the is a smaller negative number compared to the truck distance absolute modal constant is for that mode because there is no coefficient of 0.00423) estimates that rail as a mode has a ability to estimate the modal constant for the base truck mode. higher utility compared to truck as distance increases--that is, For example, the estimation that the rail modal constant is sig- its utility increases by 6 percent per mile, 0.00432/0.00397, nificant might indicate that any rail capacity, rail reliability, rail compared to truck mode, as distance increases. equipment availability, or rail customer service and handling The size of a modal coefficient, compared to all modes in a quality are important considerations in mode choice but that commodity group, indicates the preference for that mode. does not indicate the relative importance of each, nor does it Thus, for truck mode, the estimation from the RP data is that indicate the absolute utility for any of these, only the total rel- as distance increases, truck utility decreases the most for fur- ative utility compared to that of the truck mode. niture and miscellaneous products (0.01110) and decreases The results show that modal distance, whether singly or in the least for pharmaceutical and chemical products (0.00309). combination with other variables, is the most important vari- A review of the modal constants for each nontruck mode able as estimated from the RP survey. Since distance in this within each commodity group shows that for all commodity estimation serves as a surrogate for both modal cost and groups, the utility of nontruck modes compared to truck mode modal time, this is an expected finding. What the estimation is estimated to have a lower utility, which means that it is less also shows is that the size of the shipment, as indicated by the likely to be chosen than the truck mode. The size of the modal

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Table 3.7. Results of revealed-preference mode-choice estimation. Commodity Truck Truck and Rail Water Rail Water and Rail Air Statistics Group Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat SCTG 0105, Constant 0 0 -10.4 -7.72 -4.91 -14.21 -4.37 -25.83 -4.17 -23.41 -5.82 -18.15 Number of agriculture Distance -0.00423 -0.85 -0.00188 -0.41 -0.00123 -0.44 -0.00397 -0.85 -0.00127 -0.27 -0.00418 -0.74 records: products and 3,280 dist * log(kton) -0.00099 -1.75 -0.00062 -1.14 -0.00131 -2.53 -0.00020 -0.37 0.00024 0.78 -0.00269 -3.37 fish dist * 0.00069 1.11 0.00058 1.03 0.00048 0.82 0.00050 0.87 -0.00024 -0.63 0.00098 1.38 Rho- log($/ton) square: 0.866 SCTG 0609, Constant 0 0 -5.72 -30.44 #N/A #N/A -3.78 -34.79 -3.15 -21.27 -6.68 -14.72 Number of grains, Distance -0.00821 -1.68 -0.00602 -1.33 #N/A #N/A -0.00567 -1.25 -0.00606 -1.36 -0.00707 -1.2 records: alcohol, and 4,790 dist * log(kton) 0.00033 0.55 0.00041 0.73 #N/A #N/A 0.00061 1.09 -0.00177 -2.63 -0.00065 -0.85 tobacco products dist * 0.00058 1.09 0.00046 0.94 #N/A #N/A 0.00021 0.42 0.00054 1.11 0.00064 1.01 Rho- log($/ton) square: 0.820 SCTG 1014, Constant 0 0 #N/A #N/A #N/A #N/A -4.57 -27.88 -4.47 -13.98 #N/A #N/A Number of stones, Distance #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A records: nonmetallic 2,242 dist * log(kton) 0.00067 1.28 #N/A #N/A #N/A #N/A 0.00105 2.26 0.00041 0.71 #N/A #N/A minerals, and metallic ores dist * #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Rho- log($/ton) square: 0.879 SCTG 1519, Constant 0.00000 0 #N/A #N/A -3.17 -9.83 -3.66 -24.56 -4.45 -17.45 #N/A #N/A Number of coal and Distance -0.00615 -1.33 #N/A #N/A -0.00781 -2.88 -0.00653 -1.62 -0.00701 -1.63 #N/A #N/A records: petroleum 1,594, dist * log(kton) -0.00118 -3.20 #N/A #N/A 0.00059 2.66 -0.00020 -0.66 -0.00060 -1.53 #N/A #N/A products Rho- dist * 0.00109 1.83 #N/A #N/A 0.00058 2.08 0.00110 2.11 0.00132 2.4 #N/A #N/A square: log($/ton) 0.706 SCTG 2024, Constant 0 0 -1.80 -12.18 #N/A #N/A -2.73 -47.49 -1.21 -24.57 -2.76 -33.54 Number of pharmaceutical Distance -0.00309 -1.73 -0.00685 -3.62 #N/A #N/A -0.00266 -1.59 -0.00351 -2.17 -0.00642 -2.97 records: and chemical 10,302 dist * log(kton) -0.00105 -3.50 -0.00010 -0.35 #N/A #N/A -0.00020 -0.70 -0.00269 -9.62 -0.00250 -6.39 products dist * 0.00032 1.92 0.00025 1.5 #N/A #N/A 0.00001 0.09 0.00047 3.12 0.00070 3.55 Rho- log($/ton) square: 0.682 (continued on next page)

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Table 3.7. (Continued). Commodity Truck Truck and Rail Water Rail Water and Rail Air Statistics Group Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat SCTG 2530, Constant 0 0 -6.40 -37.05 #N/A #N/A -4.91 -47.38 -1.51 -37.81 -6.23 -36.67 Number of logs, wood Distance -0.00886 -3.89 -0.00601 -2.81 #N/A #N/A -0.00608 -2.91 -0.01050 -5.14 -0.00931 -3.25 records: products, and 13,689 dist * log(kton) -0.00109 -2.47 -0.00069 -1.70 #N/A #N/A -0.00046 -1.13 -0.00171 -4.22 -0.00168 -3.12 textile and leather dist * log($/ton) 0.00103 4.43 0.00074 3.39 #N/A #N/A 0.00065 3.07 0.00125 6.03 0.00122 4.18 Rho- square: 0.778 SCTG 3134, Constant 0 0 -6.95 -35.31 #N/A #N/A -4.13 -47.08 -1.36 -20.96 -4.56 -27.94 Number of base metal Distance -0.00822 -3.05 -0.00746 -2.99 #N/A #N/A -0.00885 -3.58 -0.00685 -2.76 -0.01070 -3.31 records: and 10,949 dist * log(kton) 0.00026 0.61 0.00060 1.50 #N/A #N/A 0.00101 2.50 -0.00196 -4.83 -0.00121 -27.94 machinery dist * log($/ton) 0.00083 3.25 0.00080 3.37 #N/A #N/A 0.00075 3.19 0.00082 3.47 0.00122 3.99 Rho- square: 0.785 SCTG 3538, Constant 0 0 -5.26 -41.36 #N/A #N/A -3.95 -52.78 -0.76 -22.98 -3.09 -53.35 Number of electronic, Distance -0.00725 -4.14 -0.00929 -5.58 #N/A #N/A -0.00917 -5.44 -0.00635 -3.98 -0.01070 -5.21 records: motorized 10,546 dist * log(kton) 0.00005 0.15 0.00087 2.62 #N/A #N/A 0.00081 2.41 -0.00124 -3.89 -0.00072 -1.74 vehicles, and precision dist * log($/ton) 0.00063 4.33 0.00078 5.68 #N/A #N/A -0.00917 5.51 0.00064 4.86 0.00103 6.08 Rho- instruments square: 0.575 SCTG 3943, Constant 0 0 -5.77 -34.26 #N/A #N/A -4.59 -34.32 -1.90 -40.64 -5.74 -29.15 Number of furniture, Distance -0.01110 -5.56 -0.00982 -5.26 #N/A #N/A -0.01090 -5.85 -0.01370 -7.6 -0.01690 -7.08 records: mixed freight 12,940 dist * log(kton) -0.00217 -6.14 -0.00131 -3.95 #N/A #N/A -0.00116 -3.42 -0.00265 -8.22 -0.00250 -5.95 and misc. manufactured dist * log($/ton) 0.00131 5.78 0.00110 5.14 #N/A #N/A 0.00110 5.16 0.00165 8.05 0.00201 7.49 Rho- products square: 0.836 Note: #N/A means that no value is given.

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39 constant estimates how much less useful that mode is than the to the average value for that commodity group. This allows the truck mode. Thus for the truck/rail mode, the worst utility variation of distance, which was the most significant explana- compared to truck mode is for agricultural products (10.4) tory variable singly and in combination with the other vari- and the best comparison to truck mode is for pharmaceutical ables, to be plotted and examined against observed mode and chemical products (1.8). shares in the RP data. For the agricultural products commod- When no value is given for a mode in a commodity group ity group, Figure 3.12 shows the results of varying distance on (the value is shown as #N/A), there were an insufficient num- the mode-choice estimates (shown as curves) against the ber of records in the RP data for values to be estimated for that observed mode shares (shown as bars). The model not only has mode. Thus, for the water mode, data were only sufficient to a good statistical fit, it also appears to generally match observed estimate coefficients for the agricultural products and the coal mode shares. and petroleum products commodity groups. When no value This is not the case for all commodity groups. For the stones was given for a variable within a commodity group, the RP data and ores commodity group, Figure 3.13 shows the results of did not show that this variable was a significant explanatory varying distance on the mode-choice estimates (shown as variable for that commodity group. Thus, for the stone com- curves) against the observed mode shares (shown as bars). The modity group, only the cross product of distance times the nat- model has a good statistical fit, but it does not appear to match ural logarithm of tons in thousands (ktons) was found to be a observed mode shares. It generally also overestimates the truck significant explanatory variable. mode share at large distances. As shown in Figure 3.14, the As mentioned previously, the variables for thousands of observed flows for this commodity group are most heavily annual tons shipped (ktons) and the value in dollars per tons represented by flows of less than 500 mi. An investigation was were found to be significant explanatory variables for the undertaken to see if the introduction of variables of distance mode-choice decision for any commodity group, but only as by class would approve the ability of the model to estimate the natural logarithm of that variable taken as a cross product mode choice. with distance. Thus, both the impact on the utility from ship- For the re-estimation, the distance variable was estimated ment sizes (as shown by annual ktons) and value vary with dis- according to the following formula: if the distance is less than tance, but that effect decreases, varies logarithmically, as the 500 mi, then distance1 would be equal to the distance and variable increases. As before when the coefficient of the vari- distance2 would be equal to 0; if the distance is greater than able is negative, the utility increases as the variable increases, 500 mi, then distance1 would be equal to 500 mi and dis- and when the value is negative, modal utility decreases as the tance2 would be equal to the distance minus 500 mi. Thus, variable increases. for a distance of 400 mi, distance1 would take on a value of Although the statistical ability of the estimated model to 400, and distance2 would take on a value of 0, while for a dis- explain the variation in mode choice was generally good, rang- tance of 1,600 mi, distance1 would take on a value of 500 mi, ing from a Rho-square of 0.575 to 0.879, those estimates must and distance2 would take on a value of 1,100 mi. As shown be compared against observed mode shares. An examination in Table 3.8, this did not significantly improve the model of the model estimates was made where only distance varies, by estimation. For two commodity groups, agricultural prod- setting the value in the model for ktons and dollars/ton equal ucts and wood products, shown as shaded rows in the table, 1.00 0.80 Probability 0.60 0.40 0.20 0.00 1 00 2 00 3 00 4 00 5 00 6 00 7 00 8 00 9 00 1 00 0 1 10 0 1 20 0 1 30 0 1 40 0 1 50 0 1 60 0 1 70 0 1 80 0 1 90 0 2 00 0 2 25 0 2 50 0 2 75 0 3 00 0 3 25 0 3 50 0 Distance (mile) truck truck/rail rail water/rail air Truck Truck/Rail Rail Water/Rail Air Figure 3.12. Mode share by distance for CFS Commodity Group 2 (estimated and observed).

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40 1.00 0.90 0.80 0.70 Probability 0.60 0.50 0.40 0.30 0.20 0.10 0.00 10 0 20 0 30 0 40 0 50 0 60 0 70 0 80 0 90 0 1 000 1 100 1 200 1 300 1 400 1 500 1 600 1 700 1 800 1 900 2 000 2 250 2 500 2 750 3 000 Distance (mile) truck rail water/rail Truck Rail Water/Rail Figure 3.13. Mode share by distance for CFS Commodity Group 3 (estimated and observed). the ability of the model to explain the variation in the data, are shown as curves and the observed mode shares are shown as shown by the value of Rho-squared, decreased relative to as stacked column bars. Again, the model has a good statisti- those estimations with a single-distance variable. For all cal fit, but it does not appear to match observed mode shares. other commodities, the statistical fit improved only slightly, Generally, it also overestimates the truck mode share at mid- with increases in Rho-squared, between those shown in range distances. It appears to better explain mode share below Table 3.8 and the single-distance variable estimates shown in 500 mi, but as distance increases, it still does not show the Table 3.7, of at most 0.039. That increase was in the stone expected decrease in truck mode share and increase in non- and ores commodity group. truck mode share. Figure 3.15 shows the results for the stones and ores com- Because the two-distance class estimation did not produce modity group using the two distance variable estimations. the desired results, an additional investigation was undertaken The results of varying distances on the mode-choice estimates where SCTG 14, sand and gravel, which represents most of the 40000 35000 30000 Tonnage (kton) 25000 20000 15000 10000 5000 0 0 0 0 0 0 00 00 00 00 00 50 50 10 30 50 70 90 11 13 15 17 19 22 27 Distance (mile) Truck Rail Water/Rail Figure 3.14. Volume by mode share by distance for CFS Commodity Group 3.

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Table 3.8. Results of revealed-preference mode-choice estimation with two distance classes. Commodity Truck Truck and Rail Water Rail Water and Rail Air Statistics Group Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat SCTG 0105, Constant 0 0 -18.6 -5.92 -12.10 -3.47 -9.8 -8.60 -6.06 -9.27 -9.64 -8.11 Number of agriculture Distance <500 -0.00765 -3.74 0.011900 1.26 0.0107 1.45 0.00641 1.96 -0.00105 -0.36 0.00215 0.73 records: products and 3,280 Distance >500 -0.00120 -2.13 0.00077 1.00 -0.00115 -5.37 -0.00141 2.62 -0.00060 -1.12 -0.00006 -0.09 fish Rho-square: dist * log(kton) #NA #NA #NA #NA #NA #NA #NA #NA #NA #NA #NA #NA 0.848 dist * log($/ton) #NA #NA #NA #NA #NA #NA #NA #NA #NA #NA #NA #NA SCTG 0609, Constant 0 0 -34.10 -4.71 #NA #NA -6.59 -13.25 -7.64 -7.32 -10.40 -5.83 Number of grains, Distance <500 -0.01010 -7.06 0.04940 3.35 #NA #NA -0.00104 -0.58 0.00289 1.21 0.00100 0.24 records: alcohol, and 4,790 Distance >500 -0.00119 -1.95 0.00004 0.06 #NA #NA 0.00024 0.33 -0.00002 -0.22 0.00093 0.93 tobacco Rho-square: products dist * log(kton) -0.00011 -0.85 -0.00017 -1.27 #NA #NA 0.00004 0.10 -0.00012 -1.11 -0.00146 -2.43 0.829 dist * log($/ton) -0.00032 -0.78 -0.00019 -0.50 #NA #NA -0.00044 -3.18 -0.00266 -4.97 -0.00019 -1.13 SCTG 1014, Constant 0 0 #NA #NA #NA #NA -7.31 -11.15 -8.26 -6.61 #N/A #N/A Number of stones, Distance <500 -0.01470 -4.79 #NA #NA #NA #NA -0.00550 -1.59 -0.00385 -1.08 #N/A #N/A records: nonmetallic 2,242 Distance >500 -0.00200 -1.90 #NA #NA #NA #NA -0.00530 -4.28 -0.00007 -0.19 minerals, and Rho-square: metallic ores dist * log(kton) -0.00200 -1.90 #NA #NA #NA #NA -0.00530 -4.28 -0.00007 -0.19 #N/A #N/A 0.908 dist * log($/ton) 0.00139 2.99 #NA #NA #NA #NA 0.00205 4.73 0.00020 1.50 #N/A #N/A SCTG 1519, Constant 0 0 #NA #NA -4.19 -7.31 -5.87 -11.06 -7.48 -5.58 #N/A #N/A Number of coal and Distance <500 -0.00867 -4.44 #NA #NA -0.00744 -3.26 -0.00173 -0.74 -0.00031 -0.09 #N/A #N/A records: petroleum 1,594 Distance >500 0.00091 1.08 #NA #NA -0.00537 -3.05 -0.00033 -0.33 -0.00015 -0.09 products Rho-square: dist * log(kton) -0.00122 -6.01 #NA #NA 0.00046 2.81 -0.00049 -2.96 -0.00095 -3.54 #N/A #N/A 0.725 dist * log($/ton) 0.00027 1.14 #NA #NA 0.00015 0.73 0.00027 1.21 0.00047 2.37 #N/A #N/A SCTG 2024, Constant 0 0 -8.01 -9.48 #NA #NA -5.34 -21.75 -4.12 -23.16 -6.97000 -19.90 Number of pharmaceutical Distance <500 -0.00750 -4.21 -0.00188 -0.78 #NA #NA -0.00161 -0.93 -0.00082 -0.49 -0.00274 -1.23 records: and chemical Distance >500 -0.00074 -0.39 0.00019 0.11 #NA #NA -0.00051 -0.29 -0.00172 -1.02 -0.00269 -1.2 10,302 products Rho- dist * log(kton) -0.00105 -3.25 -0.00057 -1.90 #NA #NA -0.00022 -0.72 -0.00277 -9.27 -0.00212 -5.36 square: dist * log($/ton) 0.00008 0.5 -0.00003 -0.2 #NA #NA -0.00020 -1.25 0.00025 1.59 0.00038 1.89 0.717 (continued on next page)

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Table 3.8. (Continued). Commodity Truck Truck and Rail Water Rail Water and Rail Air Statistics Group Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat SCTG 2530, Constant 0 0 -7.78 -11.97 #NA #NA -7.19 -15.13 -4.71 -23.48 -8.66 -8.58 Number of logs, wood Distance <500 -0.00659 -18.56 -0.00287 -2.00 #NA #NA -0.00158 -1.48 0.00179 3.3 -0.00005 -0.02 records: products, and 13,689 Distance >500 -0.00057 -7.94 -0.00024 -1.71 #NA #NA -0.00107 -8.71 -0.00006 -2.52 0.00028 1.61 textile and Rho-square: leather dist * log(kton) -0.00057 -7.94 -0.00024 -1.71 #NA #NA -0.00107 -8.71 -0.00006 -2.52 0.00028 1.61 0.772 dist * log($/ton) #N/A #N/A #N/A #N/A #NA #NA #N/A #N/A #N/A #N/A #N/A #N/A SCTG 3134, Constant 0 0 -12.90 -12.04 #NA #NA -5.48 -21.73 -5.78 -20.42 -7.42 -14.42 Number of base metal and Distance <500 -0.01320 -4.92 0.00027 0.08 #NA #NA -0.01060 -4.22 -0.00204 -0.8 -0.00911 -2.73 records: machinery Distance >500 -0.00771 -2.84 -0.00726 -2.89 #NA #NA -0.00876 -3.52 -0.00685 -2.74 -0.01060 -3.23 10,949 Rho- dist * log(kton) 0.00046 1.06 0.00078 1.96 #NA #NA 0.00120 2.99 -0.00165 -4.09 -0.00094 -1.66 square: dist * log($/ton) 0.00075 2.93 0.00072 3.08 #NA #NA 0.00068 2.92 0.00075 3.18 0.00113 3.69 0.792 SCTG 3538, Constant 0 0 -13.70 -13.89 #NA #NA -7.86 -17.37 -3.72 -32.45 -6.29 -32.60 Number of electronic, Distance <500 -0.01260 -7.51 0.00348 1.34 #NA #NA -0.00559 -3.00 -0.00439 -2.83 -0.00811 -4.04 records: motorized Distance >500 -0.00561 -3.22 -0.00835 -5.01 #NA #NA -0.00825 -4.88 -0.00541 -3.4 -0.00938 -4.57 10,546 vehicles, and Rho- precision dist * log(kton) 0.00013 0.42 0.00093 3.13 #NA #NA 0.00087 2.91 -0.00114 -3.98 -0.00063 -1.69 square: instruments dist * log($/ton) 0.00049 3.42 0.00066 4.86 #NA #NA 0.00064 4.69 0.00051 3.95 0.00087 5.17 0.597 SCTG 3943, Constant 0 0 -8.86 -8.16 #NA #NA -5.11 -14.94 -5.31 -26.91 -6.86 -15.24 Number of furniture, Distance <500 -0.01390 -7.19 -0.00549 -1.88 #NA #NA -0.01220 -6.08 -0.00865 -4.73 -0.01700 -6.84 records: mixed freight 12,940 and misc. Distance >500 -0.01090 -5.62 -0.00991 -5.49 #NA #NA -0.01080 -6.06 -0.01390 -8.07 -0.01700 -7.34 Rho-square: manufactured dist * log(kton) -0.00185 -5.31 -0.00100 -3.04 #NA #NA -0.00085 -2.50 -0.00226 -7.08 -0.00209 -5.04 0.841 products dist * log($/ton) -0.00185 -5.31 -0.00100 -3.04 #NA #NA -0.00085 -2.50 -0.00226 -7.08 -0.00209 -5.04 Note: #N/A means that no value is given.

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43 1.00 0.90 0.80 0.70 Probability 0.60 0.50 0.40 0.30 0.20 0.10 0.00 10 0 20 0 30 0 40 0 50 0 60 0 70 0 80 0 90 0 1 000 1 100 1 200 1 300 1 400 1 500 1 600 1 700 1 800 1 900 2 000 2 250 2 500 2 750 3 000 Distance (mile) truck rail water/rail Truck Rail Water/Rail Figure 3.15. Mode share by distance for CFS Commodity Group 3 (estimated with two distance classes and observed). short-distance flows within that commodity group, was sepa- products with shipment size and value, as logarithms of total rated for estimation and new estimates were made for the tons and total value per ton. As shown in Figure 3.17, for the remaining commodities in that group. Those results are shown distances below 500 miles, the observed flows do appear to cor- in Table 3.9. respond to the estimated mode share, but again this may be Although the model's ability to explain the variance in largely due to the dominance of truck mode share over this dis- mode choice is very high for SCTG 14, sand and gravel, when tance range. At distances greater than 500 miles, the share of treated separately, as can be seen in Figure 3.16, that is due other modes used do not fall into a discernible pattern with almost entirely to the fact that the share by modes other than distance and the estimated mode share for distances greater truck is extremely limited for this commodity at all distance than 500 miles is relatively constant above 1,500 miles. Mode ranges. Additionally, with fewer records available for the choice for the remainder of this commodity group must be estimation, the model cannot successfully estimate coeffi- assumed to be largely related to modal availability, or pro- cients for the distance across products with shipment size and cesses unique to the production and/or consumption of this value, used as logarithms of total tons and total value per ton. commodity. Those few distance ranges where other modes are observed The research has shown that it is possible to develop RP to be used do not fall into a pattern. The estimated mode databases from existing, publicly available sources. It has share for distances greater than 500 mi is relatively constant shown that modal distance, which is expected to be highly cor- above 1,500 mi. Mode choice for this commodity must be related with modal time and costs, is the single most important assumed to be largely related to modal availability or pro- consideration in mode choice. When local policy decisions can cesses unique to the production and/or consumption of this only impact the local component of modal costs and distances, commodity. and those local costs and times are only a small fraction of total For the remaining flows in this commodity group, the exclu- modal costs and distances, the difficulty of influencing mode- sion of the records for SCTG 14 results in a poorer model esti- choice decisions by local policies can be seen. The policy deci- mation, as indicated by a decline in the Rho-square compared sions that might be more subject to local control, such as to that in Table 3.8. Additionally with fewer records available, shipper, and receiver characteristics, were not found to be this estimation for the remaining commodities in this group significant variables in freight mode-choice decisions, at least cannot successfully develop coefficients for the distance across as estimated by this RP survey.

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Table 3.9. Results of revealed-preference mode-choice estimation with two distance classes (STCG 14 and remainder of stone and ore commodity group). Commodity Group Truck Truck and Rail Water Rail Water and Rail Air Statistics Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat t-stat #NA #NA #NA SCTG 14, Constant 0 0 #NA -151.00 -4.36 -9.76 -4.04 #NA #NA Number of sand and gravel Distance -0.00945 -1.26 #NA #NA #NA #NA 0.28700 3.82 0.00122 0.20 #NA #NA Records: <500 3,280 Distance -0.00174 -1.31 #NA #NA #NA #NA -0.00224 -1.42 0.00023 0.53 #NA #NA >500 #NA #NA #NA Rho-square: dist * #NA #NA #NA #NA #NA #NA #NA #NA #NA 0.954 log(kton) dist * #NA #NA #NA #NA #NA #NA #NA #NA #NA #NA #NA #NA log($/ton) #NA #NA #NA SCTG 1013, Constant 0 0 #NA -7.21 -10.66 -8.33 -6.01 #NA #NA Number of stone, Distance -0.01210 -3.81 #NA #NA #NA #NA -0.00152 -0.42 -0.00194 -0.49 #NA #NA Records: nonmetallic and <500 4,790 metallic ore #NA #NA #NA Distance -0.00110 -0.95 #NA -0.00132 -1.26 -0.00005 -0.13 #NA #NA >500 #NA #NA #NA Rho-square: dist * #NA #NA #NA #NA #NA #NA #NA #NA #NA 0.884 log(kton) dist * log #NA #NA #NA #NA #NA #NA #NA #NA #NA #NA #NA #NA ($/ton)

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45 1.00 0.90 0.80 0.70 Probability 0.60 0.50 0.40 0.30 0.20 0.10 0.00 100 200 300 400 500 600 700 80 0 90 0 1000 1 1 00 1 2 00 1300 1400 1500 1600 1700 1800 1900 2000 2250 2500 Distance (mile) truck rail water/rail Truck Rail Water/Rail Figure 3.16. Mode share by distance for CFS Commodity Group 3, SCTG 14 (estimated with two distance classes and observed). 1.00 0.90 0.80 0.70 Probability 0.60 0.50 0.40 0.30 0.20 0.10 0.00 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2250 2500 2750 3000 Distance (mile) truck rail water/rail Truck Rail Water/Rail Figure 3.17. Mode share by distance for CFS Commodity Group 3, SCTG 10-13 (estimated with two distance classes and observed).